We consider the challenging few-shot setting where models are trained on unlabelled corpora and then evaluated on target tasks with only \(k\)  examples per label (\(k \le 32\) ) to allow fine-tuning of the models on the target task.
Since our focus is on unsupervised meta-learning, we closely follow the experimental setup of [1]}.
